The operation of various systems, for example, various devices such as industrial machines, plant facilities, and network systems, requires speedy sensing of faults and as necessary an investigation of the cause. To meet this need, many diagnostic systems are conventionally proposed and practically used for sensing faults and estimating the cause of faults, based on the data measured on the parts of diagnostic objects, with various devices and systems as a diagnostic object.
For example, Patent Document 1 proposes a diagnostic system that diagnoses industrial rotary machines. The diagnostic system makes a fuzzy-pattern-matching based fault diagnosis using multiple fuzzy symptoms, obtained by making fuzzy the vibrations detected from a diagnostic object and the symptoms derived from the sound signal, as well as a fuzzy diagnostic rule that diagnoses the faults of the diagnostic object. If the fuzzy diagnostic rule does not match the state of a diagnostic object, knowledge-network-based learning is performed to create a new fuzzy diagnostic rule that receives the fuzzy symptoms and outputs the state of the diagnostic object.
Patent Document 2 proposes a diagnostic system designed for diagnostic objects that are plant facilities such as factories or power plants. This diagnostic system uses a diagnostic rule in the IF-THEN format to diagnose the condition of the facilities based on data measured on a diagnostic object. When a user enters an empirical case composed of the abnormal symptom of a diagnostic object and its degree, the occurrence frequency, and the cause, the diagnostic system generates probabilistic empirical knowledge from the empirical case and creates a new diagnostic rule from this empirical knowledge.
Patent Document 1:
    Japanese Patent Kokai Publication No. JP2002-169611APatent Document 2:    Japanese Patent Kokai Publication No. JP-A-5-307484Patent Document 3:    Japanese Patent Kokai Publication No. JP2007-018530APatent Document 4:    Japanese Patent Kokai Publication No. 2005-345154Non-Patent Document 1:    J. Takeuchi and K. Yamanishi. A unifying framework that detects outliers and change points from time series. IEEE Transactions on Knowledge and Data Engineering, 18(4):482-492,2006